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Integration of renewable deep eutectic solvents with engineered biomass to achieve a closed-loop biorefinery
Significance Deep eutectic solvents (DESs) have gained increasing attention due to their application-friendly properties, including universal solvating capabilities and wide tunability. Additionally, ease of synthesis and broad availability from inexpensive chemical components could render DESs more versatile solvents for biomass pretreatment, as compared with traditional ionic liquids. Because the long-term success of the biorefinery depends on the development of sustainable processes to convert lignocellulosics into biofuels, DESs derived from renewable sources such as lignin are highly desirable. We herein present our innovative process that integrates the use of low-recalcitrant engineered biomass with its pretreatment using lignin-derived DESs. The promising results described by near-theoretical sugar yield demonstrate the effectiveness of the integrated process, opening up opportunities toward a sustainable and circular bioeconomy.
Integrated Two-Stage Alkaline–Oxidative Pretreatment of Hybrid Poplar. Part 2: Impact of Cu-Catalyzed Alkaline Hydrogen Peroxide Pretreatment Conditions on Process Performance and Economics
Two-stage alkaline/copper 2,2'-bipyridine-catalyzed alkaline hydrogen peroxide (Cu-AHP) pretreatment is an effective strategy for improving the enzymatic digestibility of hybrid poplar. To reduce the chemical inputs and processing costs associated with this process, we investigated the effect of increasing the temperature for both the alkaline pre-extraction and the Cu-AHP pretreatment stages. The results indicate that increasing the alkaline pre-extraction and the Cu-AHP pretreatment temperatures from 30 to 120 and 80 °C, respectively, allowed us to reduce both the pretreatment time of the Cu-AHP stage and the chemical loadings. Incubating alkaline pre-extracted hybrid poplar for 12 h with 10% NaOH (w/w biomass), 8% hydrogen peroxide (w/w biomass), and a Cu 2+ and 2,2'-bipyridine (bpy) concentration of 1 mM yielded monomeric sugar yields of approximately 77% glucose and 66% xylose (based on the initial sugar composition) following enzymatic hydrolysis. Technoeconomic analysis (TEA) indicates that these changes to the two-stage alkaline/Cu-AHP pretreatment process could potentially reduce the minimum fuel selling price (MFSP) by more than $\$1.00$ per gallon of biofuel compared to the reference case where both stages were conducted at 30 °C with higher chemical inputs.
Integrated hydropyrolysis and vapor-phase hydrodeoxygenation process with Pd/Al 2 O 3 for production of advanced oxygen-containing biofuels from cellulosic wastes
This study focuses on developing advanced oxygen-containing biofuels with high furan, cyclic ketone, and ethanol content from holocellulose and cellulosic wastes. To achieve this, we employed an integrated process that combined hydropyrolysis and vapor-phase hydrodeoxygenation using Pd/Al 2 O 3 as a catalyst. In the first stage, the non-catalytic hydropyrolysis of hemicellulose resulted in furfural and acetic acid fractions that were effectively converted into furans, cyclic ketones, and ethanol in the second-stage reactor over Pd/Al 2 O 3 under 0.3 MPa H 2 . We found that a hydropyrolysis temperature of 440 °C in the first stage reactor resulted in the highest yield of oxygen-containing biofuels (171.1 mg/g) with 89.2% hemicellulose conversion. However, increasing the hydrodeoxygenation temperature in the second stage reactor reduced the yield of oxygen-containing biofuels, and at 400 °C, excess deoxygenation led to hydrocarbon production. The Pd/Al 2 O 3 catalyst demonstrated high stability during the vapor-phase hydrodeoxygenation of primary furfural and acetic acid intermediates, with only 2.1% coke formation after three reaction cycles. This scalable process enables the conversion of various cellulosic wastes into advanced oxygen-containing biofuels, with considerable total yields. Our findings suggest that this integrated process holds great promise for converting biomass waste into advanced oxygen-containing biofuels.
Integrating field observations and process-based modeling to predict watershed water quality under environmental perturbations
Watersheds play a critical role in supplying water resources needed for human use and ecosystem health. Understanding and predicting how, when, and where changes in the quantity and quality of water resources occur under different environmental stresses including extreme events is crucial for sustainable management of water resources under a changing environment. However, few studies have attempted to quantify or identify the factors and process interactions controlling the impact of extreme events across water-shed systems. Only few large-scale studies include coordinated monitoring and modeling efforts, which limits our ability to assess the large-scale impact of extreme events on water supply and quality. Methods are lacking to propagate uncertainty in process understanding through an integrated hydro-biogeochemical model framework and evaluate its importance, thus failing to take full advantage of the information potentially available through transformative advances in characterization technologies from high-resolution mass spectrometry to airborne and satellite-based remote sensing. There are consequent risks to our nations water security and to human and ecosystem health that may become exacerbated with the increasing frequency of extreme events that is projected for the coming decades. This paper reviews the current status of watershed science for both water quantity and quality and identifies critical gaps in our current knowledge and modeling capability in addressing the emergent needs in predicting watershed hydrologic and biogeochemical responses (i.e., water quantity and quality) under natural and anthropogenic perturbations. We highlight the need to (1) understand how environmental perturbations including extreme events like floods and droughts propagate through watershed systems and assess their short- and long-term impacts on watershed biogeochemistry, water quality and their recovery pathways; (2) develop and improve a watershed water quality model that reflects the state of scientific understanding gained from observations; and (3) construct a data-model fusion system for watershed characterization, process identification, and mechanistic model parameterization. A large base of modeling, monitoring and data capabilities have been built by various federal government agencies given the relevance of water to their critical missions. An emerging need is to build an integrated national capability for watershed water availability and quality that can address water-related missions across multiple federal agencies.
MICROREACTOR APPLICATIONS, RESEARCH, VALIDATION, AND EVALUATION (MARVEL) REACTOR ? STATUS, CONSTRUCTION, AND TESTING
The paper presents the current status of the Microreactor Applications, Research, Validation, and Evaluation (MARVEL) microreactor design, qualification testing, fabrication, and high-level construction schedule. An overview of initial criticality, low power physics testing, and start-up testing is included, as well as an overview of the envisioned processes in which end-users can engage the project for access to operational data or specific demonstrations. Designed by the Idaho National Laboratory (INL) under the auspices of the US Department of Energy’s Microreactor Program for construction and operation at the INL, MARVEL is a small, fully functional advanced reactor with UZrH fuel and thermal output of 85 kW. It offers a unique opportunity for scaled demonstrations that can dramatically accelerate the design, licensing, and deployment of commercial microreactors for power production or process heat applications. MARVEL’s objective is to build a small liquid-metal thermal reactor at the INL to demonstrate design and operating processes for microreactors, microgrid integration, and process heat applications. MARVEL finished 90%-final-design in September 2023 and completed an independent project assessment in early 2024. Fabrication of long-lead components and fuel, safety analysis review, and procurement for construction are underway. MARVEL assembly and construction will start in 2025 and fuel loading is expected in mid- 2027. Initial criticality will be performed in a dry condition in late 2027, followed by loading of NaK coolant and start-up testing. Approximately six months later, release for unrestricted operations will enable subsequent testing of microreactor characteristics, microgrid integration and select heat extraction applications.
Ferroelectric epitaxial Al(Sc/B)N/Mo/SiC heterostructures for high operating temperature devices
Advances in wurtzite nitride ferroelectrics of Al 1-x M x N ( M = Sc or B) have led to novel capabilities, which must be integrated into existing fabrication processes. In the case of electronics operating > 200°C, a movement toward SiC-based platforms enables better performance over conventional Si, primarily due to the higher bandgap and lower intrinsic carrier concentration of SiC. Hence, the challenge is to develop a deposition process to integrate Al 1-x M x M x N ferroelectrics with elevated temperature-compatible materials for high temperature electronics such as the non-volatile memory component. We demonstrate epitaxial Al M N/Mo/SiC heterostructures, which provides both the crystalline and surface features that promote high-quality ferroelectric nitride film growth. Omega scans of the Mo (110) reflection exhibit a full width at half max of < 0.02° (40 arc sec) and the (0002) peak of the subsequently grown nitride film had a value of 1.1° for 160 nm thick Al 0.7 Sc 0.3 N and 1.3° for 400 nm thick Al 0.94 B 0.06 N. The crystallographic relationships found between the Al M N, Mo, and SiC layers indicate an advancement in sputter deposition of epitaxial films. Ferroelectric switching is also shown at 400 °C in both samples via polarization-electric field hysteresis and pulsed measurements, which exhibited P r values >100 μC cm -2 and E c between 3 and 4 M V cm -1 , despite a large presence of oxygen in both Al M N films ranging between 4 and 5 at.%, revealed by compositional analysis. This study demonstrates the process for synthesizing high-crystal quality ferroelectric nitride films, which can be used in extremely high temperature applications.
Alternatives to MARVEL Power Conversion -- Comparison of Stirling Engine Thermal Efficiency and Design to Other Power Conversion Cycles
PRESENTATION for CONFERENCE PAPER: The Microreactor Applications, Research, Validation, and Evaluation (MARVEL) Reactor is a small liquid-metal thermal reactor that will be built at the Idaho National Laboratory to demonstrate design and operating processes for microreactors, microgrid integration, and process heat applications. Power conversion in the MARVEL design is provided by Stirling engines, which have disadvantages in nuclear environments. Compared to Stirling engine performance, some alternative power cycles can increase power production when coupled to a liquid-metal thermal reactor. In this paper, the thermal efficiency of MARVEL’s power production with Stirling engines is compared to the thermal efficiency of power production with MARVEL and alternative power cycles. Those cycles include a superheated Rankine cycle, open and closed Brayton cycles, and a supercritical carbon dioxide cycle. All cycles (except the Stirling engines) were modeled with an intermediate helium loop to meet MARVEL’s principal design criteria. All models are simple designs with conservative assumptions for consistent comparison. Detailed optimization will depend largely on reactor location and application, and the relative merit of each cycle is discussed for different environmental conditions. The study informs significant early decisions on power cycle design and economics for deployment of advanced microreactors as they move from theory and concept to execution.
Alternatives to MARVEL Power Conversion – Comparison of Stirling Engine Thermal Efficiency and Design to other Power Conversion Cycles
The Microreactor Applications, Research, Validation, and Evaluation (MARVEL) Reactor is a small liquid-metal thermal reactor that will be built at the Idaho National Laboratory to demonstrate design and operating processes for microreactors, microgrid integration, and process heat applications. Power conversion in the MARVEL design is provided by Stirling engines, which have disadvantages in nuclear environments. Compared to Stirling engine performance, some alternative power cycles can increase power production when coupled to a liquid-metal thermal reactor In this paper, the thermal efficiency of MARVEL’s power production with Stirling engines is compared to the thermal efficiency of power production with MARVEL and alternative power cycles. Those cycles include a superheated Rankine cycle, open and closed Brayton cycles, and a supercritical carbon dioxide cycle. All cycles (except the Stirling engines) were modeled with an intermediate helium loop to meet MARVEL’s principal design criteria. All models are simple designs with conservative assumptions for consistent comparison. Detailed optimization will depend largely on reactor location and application, and the relative merit of each cycle is discussed for different environmental conditions. The study informs significant early decisions on power cycle design and economic
Driving towards cost-competitive biofuels through catalytic fast pyrolysis by rethinking catalyst selection and reactor configuration
Catalytic fast pyrolysis (CFP) has emerged as an attractive process for the conversion of lignocellulosic biomass into renewable fuels and products. Considerable research and development has focused on using circulating-bed reactors with zeolite catalysts (e.g., HZSM-5) for CFP because of their propensity to form gasoline-range aromatic hydrocarbons. However, the high selectivity for aromatics comes at the expense of low carbon yield, a key economic driver for this process. In this contribution, we evaluate non-zeolite catalysts in a fixed-bed reactor configuration for an integrated CFP process to produce fuel blendstocks from lignocellulosic biomass. These experimental efforts are coupled with technoeconomic analysis (TEA) to benchmark the process and guide research and development activities to minimize costs. The results indicate that CFP bio-oil can be produced from pine with improved yield by using a bifunctional metal-acid 2 wt% Pt/TiO 2 catalyst in a fixed-bed reactor operated with co-fed H 2 at near atmospheric pressure, as compared to H-ZSM5 in a circulating-bed reactor. The Pt/TiO 2 catalyst exhibited good stability over 13 reaction-regeneration cycles with no evidence of irreversible deactivation. The CFP bio-oil was continuously hydrotreated for 140 h time-on-stream using a single-stage system with 84 wt% of the hydrotreated product having a boiling point in the gasoline and distillate range. This integrated biomass-to-blendstock process was determined to exhibit an energy efficiency of 50% and a carbon efficiency of 38%, based on the experimental results and process modelling. TEA of the integrated process revealed a modelled minimum fuel selling price (MFSP) of $4.34 per gasoline gallon equivalent (GGE), which represents a cost reduction of $0.85 GGE -1 compared to values reported for CFP with a zeolite catalyst. TEA also indicated that catalyst cost was a significant factor influencing the MFSP, which informed additional CFP experiments in which lower-cost Mo2C and high-dispersion 0.5 wt% Pt/TiO 2 catalysts were synthesized and evaluated. These materials demonstrated CFP carbon yield and oil oxygen content similar to those of the 2 wt% Pt/TiO 2 catalyst, offering proof-of-concept that the lower-cost catalysts can be effective for CFP and providing a route to reduce the modelled MFSP to $3.86–3.91 GGE -1 . This report links foundational science and applied engineering to demonstrate the potential of fixed-bed CFP and highlights the impact of coupled TEA to guide research activities towards cost reductions.
Wafer-scale waveguide sidewall roughness scattering loss characterization by image processing
Photonic integrated circuits (PICs) are vital for developing affordable, high-performance optoelectronic devices that can be manufactured at an industrial scale, driving innovation and efficiency in various applications. Optical loss of modes in thin film waveguides and devices is a critical measure of their performance. Thin film growth, lithography, masking, and etching processes are imperfect processes that introduce significant sidewall and top-surface roughness and cause dominating optical losses in waveguides and photonic structures. This roughness, as perturbations couple light from guided to far-field radiation modes, leads to scattering losses that can be estimated from theoretical models. Typically, with UV-based lithography, sidewall roughness is significantly larger than wafer-top surface roughness. Atomic force microscopy (AFM) imaging measurement gives a 3D and high-resolution roughness profile, but the measurement is inconvenient, costly, and unscalable for large-scale PICs and at wafer-scale. Here, we evaluate the sidewall roughness profile based on 2D high-resolution scanning electron microscope (SEM) imaging. We characterized the loss on two homemade nitride and oxide films on 3-inch silicon wafers with 12 waveguide devices on each and correlated the scattering loss estimated from a 2D image-based sidewall profile and theoretical Payne model. The lowest loss of guided fundamental transverse electric (TE 0 ) mode is found at 0.075 dB/cm at 633 nm across 24 devices, a record at visible wavelength. Our work shows 100% success (edge continuity span exceeding 95% of image width/height) in edge detection in image processing of all images to estimate autocorrelation function and optical mode loss. These demonstrations offer valuable insights into waveguide sidewall roughness and a comparison of experimental and 2D SEM image processing based loss estimations with applications in loss characterization at wafer-scale PICs.
Integrating terrestrial and aquatic processes toward watershed scale modeling of dissolved organic carbon fluxes
Dissolved organic carbon (DOC) is not only a critical component of global and regional carbon budgets, but also an important precursor for carcinogenic disinfection byproducts (DBP) generated during drinking water disinfection process. The lack of process based watershed scale model for carbon cycling has been a limiting factor impeding effective watershed management to control DOC fluxes to source waters. Here, we integrated terrestrial and aquatic carbon processes into the widely tested Soil and Water Assessment Tool (SWAT) watershed model to enable watershed-scale DOC modeling (referred to as SWAT-DOC hereafter). The modifications to SWAT mainly fall into two groups: (1) DOC production in soils and its transport to aquatic environment by different hydrologic processes, and (2) riverine transformation of DOC and their interactions with particular organic carbon (POC), inorganic carbon and algae (floating and bottom). We tested the new SWAT-DOC model in the Cannonsville watershed, which is part of the New York City (NYC) water supply system, using long-term DOC load data (from 1998 to 2012) derived from 1399 DOC samplings. The calibration and verification results indicate that SWAT-DOC achieved satisfactory performance for both streamflow and DOC at daily and monthly temporal scales. The parameter sensitivity analysis indicates that DOC loads in the Cannonsville watershed are controlled by the DOC production in soils and its transport in both terrestrial and aquatic environments. Further model uncertainty analysis indicates high uncertainties associated with peak DOC loads, which are attributed to underestimation of high streamflows. Therefore, future efforts to enhance SWAT-DOC to better represent runoff generation processes hold promise to further improve DOC load simulation. Overall, the wide use of SWAT and the satisfactory performance of SWAT-DOC make it a useful tool for DOC modeling and mitigation at the watershed scale.
Integrating Cybersecurity Risk Assessment with Process Safety in Chemical Process Industries
This dissertation bridges the gap between industrial cybersecurity and traditional process safety by introducing an integrated Cyber-LOPA framework that combines the Purdue Enterprise Reference Architecture, Cyber Kill Chain, and CVSS v4.0 metrics. Demonstrated on a High-Density Polyethylene slurry process, the work illustrates how cyber threats targeting automation systems can bypass physical protection layers, proving the necessity of unified risk assessments to prevent cyber-induced physical incidents in chemical manufacturing plants.
The Power of Priors: Improved Enrichment Safeguards
International safeguards currently rely on material accountancy to verify that declared nuclear material is present and unmodified. Although effective, material accountancy for large bulk facilities can be expensive to implement due to the high precision instrumentation required to meet regulatory targets. Process monitoring has long been considered to improve material accountancy. However, effective integration of process monitoring has been met with mixed results. Given the large successes in other domains, machine learning may present a solution for process monitoring integration. Past work has shown that unsupervised approaches struggle due to measurement error. Although not studied in depth for a safeguards context, supervised approaches often have poor generalization for unseen classes of data (e.g., unseen material loss patterns). This work shows that engineered datasets, when used for training, can improve the generalization of supervised approaches. Further, the underlying models needed to generate these datasets need only accurately model certain high importance features.
Integrated RMP-based ELM-crash-control process for plasma performance enhancement during ELM crash suppression in KSTAR
The integrated Resonant Magnetic Perturbation (RMP)-based Edge-Localized Mode (ELM)-crash-control process aims to enhance the plasma performance during the RMP-driven ELM crash suppression, where the RMP induces an unwanted confinement degradation. In this study, the normalized beta ($\beta_\textrm{N}$) is introduced as a metric for plasma performance. The integrated process incorporates the latest achievements in the RMP technique to enhance $\beta_\textrm{N}$ efficiently. The integrated process triggers the n = 1 Edge-localized RMP (ERMP) at the L–H transition timing using the real-time Machine Learning (ML) classifier. The pre-emptive RMP onset can reduce the required external heating power for achieving the same $\beta_\textrm{N}$ by over 10% compared to the conventional onset. During the RMP phase, the adaptive feedback RMP ELM controller, demonstrating its performance in previous experiments, plays a crucial role in maximizing $\beta_\textrm{N}$ during the suppression phase and sustaining the $\beta_\textrm{N}$-enhanced suppression state by optimizing the RMP strength. The integrated process achieves $\beta_\textrm{N}$ up to ~2.65 during the suppression phase, which is ~10% higher than the previous KSTAR record but ~6% lower than the target of the K-DEMO first phase ($\beta_\textrm{N}$ = 2.8), and maintains the suppression phase above the lower limit of target $\beta_\textrm{N}$ (= 2.4) for ~4 s (~60$\tau_\textrm{E}$). In addition to $\beta_\textrm{N}$ enhancement, the integrated process demonstrates quicker restoration of the suppression phase and recovery of $\beta_\textrm{N}$ compared to the adaptive control with the n = 1 Conventional RMP (CRMP). The post-analysis of the experiment shows the localized effect of the ERMP spectrum in radial and the close relationship between the evolution of $\beta_\textrm{N}$ and the electron temperature.
Optimization of energy requirements for CO 2 post-combustion capture process through advanced thermal integration
The energy optimization modeling work described here was performed to determine efficiency improvements that could be achieved for existing coal-fired power plants to retrofit a partial CO 2 capture from the post-combustion flue gas for carbon sequestration through thermal integration. The work presented includes optimization of the mono-ethanol amine (MEA)-based post-combustion CO 2 capture to reduce energy requirements that could be achieved at existing power plants by thermal integration of the steam turbine cycle, boiler, CO 2 compression train and post-combustion CO 2 capture process to offset efficiency and capacity losses that would be incurred by retrofit or implementation of post-combustion CO 2 capture. Furthermore, partial CO 2 capture, involving treatment of less than 100% of the flue gas leaving the plant and modular design of the CO 2 scrubbing system, was also investigated. Thermal integration of the steam turbine cycle with boiler and CO 2 compression train improved cycle and plant performance and offset, in part, the negative effects of post-combustion CO 2 capture. The best-analyzed integration options improved gross power output by 5% and net unit efficiency by 1.57%, relative to the conventional MEA process. Operating with 40% CO 2 capture increased gross power output by 11.6–14% (depending on the MEA thermal integration option), relative to the conventional MEA integration and 90% CO 2 capture. The improvement in net unit performance is larger compared to the improvement in turbine cycle performance because of the CO 2 compression work, which is also reduced by partial CO 2 capture.
Improving the performance of medical imaging applications using SYCL
As opposed to the Open Computing Language (OpenCL) programming model, in which host and device codes are generally written in different programming languages, SYCL can combine host and device codes for an application in a type-safe way to improve development productivity and performance portability. Hence, this report shows the experimental results of applying the SYCL programming model to medical imaging applications for a study on performance portability and programming productivity between OpenCL and SYCL. Rodinia is a widely used open-source benchmark suite for heterogeneous computing. We choose two medical imaging applications (Heart Wall and Particle Filter) in the benchmark suite, migrate the OpenCL implementations of the applications to the SYCL implementations, and evaluate their performance and productivity on Intel® microprocessors that contain a central processing unit (CPU) and an integrated graphics processing unit (GPU). The maturing SYCL compilers, which are based on a conformant implementation of the SYCL 1.2.1 Khronos specification, have been actively optimized for Intel® computing platforms. The experimental results are promising in terms of the raw performance and productivity. Although the SYCL implementation of the Heart Wall application does not execute successfully on a CPU, the SYCL implementations of the application can achieve comparable or better performance than the OpenCL implementation on Intel® integrated GPUs. For the Particle Filter application, the performance difference between the SYCL and OpenCL implementations is comparable on the GPUs for most cases, but the SYCL implementations are on average 4X faster than the OpenCL implementations on Intel® Xeon® CPUs. For programming productivity, we arguably use lines of code as a way to measure programming productivity in software. The SYCL programs reduce the lines of code of the OpenCL programs by 52% and 38% for the Heart Wall and Particle Filter, respectively. The results indicate that SYCL is a promising programming model for heterogeneous computing with the maturing compilers. We organize the remainder of the report as follows. Section II introduces the SYCL programming model, compares the major differences between an OpenCL program and a SYCL program, and describes the characteristics of the two applications. Section III describes the SYCL programming model in more details, and shows the SYCL implementation of a kernel function in the Particle Filter application as an example. In Section IV, we evaluate the performance of the applications on the CPUs and GPUs. Section V concludes the report.
Developing a predictive science of the biosphere requires the integration of scientific cultures
Increasing the speed of scientific progress is urgently needed to address the many challenges associated with the biosphere in the Anthropocene. Consequently, the critical question becomes: How can science most rapidly progress to address large, complex global problems? We suggest that the lag in the development of a more predictive science of the biosphere is not only because the biosphere is so much more complex, or because we do not have enough data, or are not doing enough experiments, but, in large part, because of unresolved tension between the three dominant scientific cultures that pervade the research community. We introduce and explain the concept of the three scientific cultures and present a novel analysis of their characteristics, supported by examples and a formal mathematical definition/representation of what this means and implies. The three cultures operate, to varying degrees, across all of science. However, within the biosciences, and in contrast to some of the other sciences, they remain relatively more separated, and their lack of integration has hindered their potential power and insight. Our solution to accelerating a broader, predictive science of the biosphere is to enhance integration of scientific cultures. The process of integration—Scientific Transculturalism—recognizes that the push for interdisciplinary research, in general, is just not enough. Unless these cultures of science are formally appreciated and their thinking iteratively integrated into scientific discovery and advancement, there will continue to be numerous significant challenges that will increasingly limit forecasting and prediction efforts.